The objective of semantic correspondence is to find correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations [1]}, [2]}, [3]}. This is highly similar to few-shot segmentation setting in that few-shot segmentation also aims to label the objects of same class with large intra-class variations, and thus the recent works in both tasks have been taking similar approaches. The latest approaches [4]}, [5]}, [6]}, [7]}, [1]}, [9]}, [3]}, [2]} in semantic correspondence focused on cost aggregation stage to find reliable correspondences, and proved its importance. Among those, [3]} proposed to use 4D convolutions for cost aggregation while it showed apparent limitations which include limited receptive fields of convolutions. CATs [2]} resolve this issue and sets a new state-of-the-art by leveraging transformers [14]} to aggregate the cost volume. However, it suffers from high computation due to high dimensional nature of correlation map. In this paper, we propose to resolve the aforementioned issues.
